"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def 	snake_case__					(			_snake_case				:       Optional[int]     ):

      """simple docstring"""

      UpperCamelCase__										=			SwinvaConfig()
      UpperCamelCase__										=			swinva_name.split("_"     )

      UpperCamelCase__										=			name_split[1]
      if "to" in name_split[3]:
            UpperCamelCase__										=			int(name_split[3][-3:]     )
      else:
            UpperCamelCase__										=			int(name_split[3]     )
      if "to" in name_split[2]:
            UpperCamelCase__										=			int(name_split[2][-2:]     )
      else:
            UpperCamelCase__										=			int(name_split[2][6:]     )

      if model_size == "tiny":
            UpperCamelCase__										=			96
            UpperCamelCase__										=			(2, 2, 6, 2)
            UpperCamelCase__										=			(3, 6, 12, 24)
      elif model_size == "small":
            UpperCamelCase__										=			96
            UpperCamelCase__										=			(2, 2, 18, 2)
            UpperCamelCase__										=			(3, 6, 12, 24)
      elif model_size == "base":
            UpperCamelCase__										=			1_28
            UpperCamelCase__										=			(2, 2, 18, 2)
            UpperCamelCase__										=			(4, 8, 16, 32)
      else:
            UpperCamelCase__										=			1_92
            UpperCamelCase__										=			(2, 2, 18, 2)
            UpperCamelCase__										=			(6, 12, 24, 48)

      if "to" in swinva_name:
            UpperCamelCase__										=			(12, 12, 12, 6)

      if ("22k" in swinva_name) and ("to" not in swinva_name):
            UpperCamelCase__										=			2_18_41
            UpperCamelCase__										=			"""huggingface/label-files"""
            UpperCamelCase__										=			"""imagenet-22k-id2label.json"""
            UpperCamelCase__										=			json.load(open(hf_hub_download(a__							,      a__							,      repo_type="dataset"     )							,      "r"     )     )
            UpperCamelCase__										=			{int(a__     ): v for k, v in idalabel.items()}
            UpperCamelCase__										=			idalabel
            UpperCamelCase__										=			{v: k for k, v in idalabel.items()}

      else:
            UpperCamelCase__										=			10_00
            UpperCamelCase__										=			"""huggingface/label-files"""
            UpperCamelCase__										=			"""imagenet-1k-id2label.json"""
            UpperCamelCase__										=			json.load(open(hf_hub_download(a__							,      a__							,      repo_type="dataset"     )							,      "r"     )     )
            UpperCamelCase__										=			{int(a__     ): v for k, v in idalabel.items()}
            UpperCamelCase__										=			idalabel
            UpperCamelCase__										=			{v: k for k, v in idalabel.items()}

      UpperCamelCase__										=			img_size
      UpperCamelCase__										=			num_classes
      UpperCamelCase__										=			embed_dim
      UpperCamelCase__										=			depths
      UpperCamelCase__										=			num_heads
      UpperCamelCase__										=			window_size

      return config
def 	snake_case__					(			_snake_case				:       Optional[int]     ):

      """simple docstring"""

      if "patch_embed.proj" in name:
            UpperCamelCase__										=			name.replace("patch_embed.proj"							,      "embeddings.patch_embeddings.projection"     )
      if "patch_embed.norm" in name:
            UpperCamelCase__										=			name.replace("patch_embed.norm"							,      "embeddings.norm"     )
      if "layers" in name:
            UpperCamelCase__										=			"""encoder.""" + name
      if "attn.proj" in name:
            UpperCamelCase__										=			name.replace("attn.proj"							,      "attention.output.dense"     )
      if "attn" in name:
            UpperCamelCase__										=			name.replace("attn"							,      "attention.self"     )
      if "norm1" in name:
            UpperCamelCase__										=			name.replace("norm1"							,      "layernorm_before"     )
      if "norm2" in name:
            UpperCamelCase__										=			name.replace("norm2"							,      "layernorm_after"     )
      if "mlp.fc1" in name:
            UpperCamelCase__										=			name.replace("mlp.fc1"							,      "intermediate.dense"     )
      if "mlp.fc2" in name:
            UpperCamelCase__										=			name.replace("mlp.fc2"							,      "output.dense"     )
      if "q_bias" in name:
            UpperCamelCase__										=			name.replace("q_bias"							,      "query.bias"     )
      if "k_bias" in name:
            UpperCamelCase__										=			name.replace("k_bias"							,      "key.bias"     )
      if "v_bias" in name:
            UpperCamelCase__										=			name.replace("v_bias"							,      "value.bias"     )
      if "cpb_mlp" in name:
            UpperCamelCase__										=			name.replace("cpb_mlp"							,      "continuous_position_bias_mlp"     )
      if name == "norm.weight":
            UpperCamelCase__										=			"""layernorm.weight"""
      if name == "norm.bias":
            UpperCamelCase__										=			"""layernorm.bias"""

      if "head" in name:
            UpperCamelCase__										=			name.replace("head"							,      "classifier"     )
      else:
            UpperCamelCase__										=			"""swinv2.""" + name

      return name
def 	snake_case__					(			_snake_case				:       Optional[int]							,      _snake_case				:       Dict     ):

      """simple docstring"""

      for key in orig_state_dict.copy().keys():
            UpperCamelCase__										=			orig_state_dict.pop(a__     )

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  UpperCamelCase__										=			key.split("."     )
                  UpperCamelCase__										=			int(key_split[1]     )
                  UpperCamelCase__										=			int(key_split[3]     )
                  UpperCamelCase__										=			model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        UpperCamelCase__										=			val[:dim, :]
                        UpperCamelCase__										=			val[dim : dim * 2, :]
                        UpperCamelCase__										=			val[-dim:, :]
                  else:
                        UpperCamelCase__										=			val[:dim]
                        UpperCamelCase__										=			val[
                            dim : dim * 2
                        ]
                        UpperCamelCase__										=			val[-dim:]
            else:
                  UpperCamelCase__										=			val

      return orig_state_dict
def 	snake_case__					(			_snake_case				:       Any							,      _snake_case				:       List[str]     ):

      """simple docstring"""

      UpperCamelCase__										=			timm.create_model(a__							,      pretrained=a__     )
      timm_model.eval()

      UpperCamelCase__										=			get_swinva_config(a__     )
      UpperCamelCase__										=			SwinvaForImageClassification(a__     )
      model.eval()

      UpperCamelCase__										=			convert_state_dict(timm_model.state_dict()							,      a__     )
      model.load_state_dict(a__     )

      UpperCamelCase__										=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""

      UpperCamelCase__										=			AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"							,      "-"     )     )     )
      UpperCamelCase__										=			Image.open(requests.get(a__							,      stream=a__     ).raw     )
      UpperCamelCase__										=			image_processor(images=a__							,      return_tensors="pt"     )

      UpperCamelCase__										=			timm_model(inputs["pixel_values"]     )
      UpperCamelCase__										=			model(**a__     ).logits

      assert torch.allclose(a__							,      a__							,      atol=1E-3     )

      print(F'Saving model {swinva_name} to {pytorch_dump_folder_path}'     )
      model.save_pretrained(a__     )

      print(F'Saving image processor to {pytorch_dump_folder_path}'     )
      image_processor.save_pretrained(a__     )

      model.push_to_hub(
          repo_path_or_name=Path(a__							,      a__     )							,      organization="nandwalritik"							,      commit_message="Add model"							,      )


if __name__ == "__main__":
   A :    Dict										=      argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--swinv2_name',
       default='swinv2_tiny_patch4_window8_256',
       type=str,
       help='Name of the Swinv2 timm model you\'d like to convert.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )

   A :    Union[str, Any]										=      parser.parse_args()
   convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)


import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				:      Union[str, Any]   		=			logging.getLogger(__name__)



@dataclass
class        A__(a_							):
						"""simple docstring"""






						_A    :				Optional[float]       					=			field(
						    default=0.0,  metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''Whether to SortishSamler or not.'''}							)
						_A    :				bool       					=			field(
						    default=a_,  metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''whether to use adafactor'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(default=a_,  metadata={'''help''': '''Dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[str]       					=			field(
						    default='''linear''',  metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()							)}'''},  )


